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Decision Tree

In this lab exercise, you will learn a popular machine learning algorithm, Decision Tree. You will use this classification algorithm to build a model from historical data of patients, and their respond to different medications. Then you use the trained decision tree to predict the class of a unknown patient, or to find a proper drug for a new patient.

Import the Following Libraries:

  • numpy (as np)
  • pandas
  • DecisionTreeClassifier from sklearn.tree
In [1]:
import numpy as np 
import pandas as pd
from sklearn.tree import DecisionTreeClassifier

About dataset

Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y.

Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to.

It is a sample of binary classifier, and you can use the training part of the dataset to build a decision tree, and then use it to predict the class of a unknown patient, or to prescribe it to a new patient.

Downloading Data

To download the data, we will use !wget to download it from IBM Object Storage.

In [2]:
#!wget -O drug200.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv

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now, read data using pandas dataframe:

In [3]:
my_data = pd.read_csv("drug200.csv", delimiter=",")
my_data[0:5]
Out[3]:
Age Sex BP Cholesterol Na_to_K Drug
0 23 F HIGH HIGH 25.355 drugY
1 47 M LOW HIGH 13.093 drugC
2 47 M LOW HIGH 10.114 drugC
3 28 F NORMAL HIGH 7.798 drugX
4 61 F LOW HIGH 18.043 drugY

Size, Type of Data

What is the size of data? What is the type of data?

In [4]:
#Size of Data

print(my_data.shape)

(200, 6)
In [5]:
#Type of Data

my_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 6 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Age          200 non-null    int64  
 1   Sex          200 non-null    object 
 2   BP           200 non-null    object 
 3   Cholesterol  200 non-null    object 
 4   Na_to_K      200 non-null    float64
 5   Drug         200 non-null    object 
dtypes: float64(1), int64(1), object(4)
memory usage: 9.5+ KB

Pre-processing

Using my_data as the Drug.csv data read by pandas, declare the following variables:

  • X as the Feature Matrix (data of my_data)
  • y as the response vector (target)

Remove the column containing the target name since it doesn't contain numeric values.

In [6]:
X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values
X[0:5]
Out[6]:
array([[23, 'F', 'HIGH', 'HIGH', 25.355],
       [47, 'M', 'LOW', 'HIGH', 13.093],
       [47, 'M', 'LOW', 'HIGH', 10.114],
       [28, 'F', 'NORMAL', 'HIGH', 7.798],
       [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)

As you may figure out, some featurs in this dataset are catergorical such as Sex or BP. Unfortunately, Sklearn Decision Trees do not handle categorical variables. But still we can convert these features to numerical values. pandas.get_dummies() Convert categorical variable into dummy/indicator variables.

In [7]:
from sklearn import preprocessing
le_sex = preprocessing.LabelEncoder()
le_sex.fit(['F','M'])
X[:,1] = le_sex.transform(X[:,1]) 


le_BP = preprocessing.LabelEncoder()
le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])
X[:,2] = le_BP.transform(X[:,2])


le_Chol = preprocessing.LabelEncoder()
le_Chol.fit([ 'NORMAL', 'HIGH'])
X[:,3] = le_Chol.transform(X[:,3]) 

X[0:5]
Out[7]:
array([[23, 0, 0, 0, 25.355],
       [47, 1, 1, 0, 13.093],
       [47, 1, 1, 0, 10.114],
       [28, 0, 2, 0, 7.798],
       [61, 0, 1, 0, 18.043]], dtype=object)

Now we can fill the target variable.

In [8]:
y = my_data["Drug"]
y[0:5]
Out[8]:
0    drugY
1    drugC
2    drugC
3    drugX
4    drugY
Name: Drug, dtype: object

Setting up the Decision Tree

We will be using train/test split on our decision tree. Let's import train_test_split from sklearn.cross_validation.

In [9]:
from sklearn.model_selection import train_test_split

Now train_test_split will return 4 different parameters. We will name them:
X_trainset, X_testset, y_trainset, y_testset

The train_test_split will need the parameters:
X, y, test_size=0.3, and random_state=3.

The X and y are the arrays required before the split, the test_size represents the ratio of the testing dataset, and the random_state ensures that we obtain the same splits.

In [10]:
X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)

The Shape of Trainset and Testset

Print the shape of X_trainset and y_trainset. Ensure that the dimensions match

In [11]:
#The shape of Trainset

print ("Ukuran X_trainset = ", X_trainset.shape)
print ("Ukuran y_trainset = ", y_trainset.shape)

Ukuran X_trainset =  (140, 5)
Ukuran y_trainset =  (140,)

Print the shape of X_testset and y_testset. Ensure that the dimensions match

In [12]:
#The shape of Testset

print ("Ukuran X_testset = ", X_testset.shape)
print ("Ukuran y_testset = ", y_testset.shape)

Ukuran X_testset =  (60, 5)
Ukuran y_testset =  (60,)

Modeling

We will first create an instance of the DecisionTreeClassifier called drugTree.
Inside of the classifier, specify criterion="entropy" so we can see the information gain of each node.

In [13]:
drugTree = DecisionTreeClassifier(criterion="gini", max_depth = 10)
drugTree # it shows the default parameters
Out[13]:
DecisionTreeClassifier(max_depth=10)

Next, we will fit the data with the training feature matrix X_trainset and training response vector y_trainset

In [14]:
drugTree.fit(X_trainset,y_trainset)
Out[14]:
DecisionTreeClassifier(max_depth=10)

Prediction

Let's make some predictions on the testing dataset and store it into a variable called predTree.

In [15]:
predTree = drugTree.predict(X_testset)

You can print out predTree and y_testset if you want to visually compare the prediction to the actual values.

In [16]:
print (predTree [0:5])
print (y_testset [0:5])

['drugY' 'drugX' 'drugX' 'drugX' 'drugX']
40     drugY
51     drugX
139    drugX
197    drugX
170    drugX
Name: Drug, dtype: object

Evaluation

Next, let's import metrics from sklearn and check the accuracy of our model.

In [17]:
from sklearn import metrics
import matplotlib.pyplot as plt
print("DecisionTrees's Accuracy: ", metrics.accuracy_score(y_testset, predTree))

DecisionTrees's Accuracy:  0.9833333333333333

Accuracy classification score computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.

In multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.

Visualization

Lets visualize the tree. Install the Library first.

We need the library:

  • pydotplus
  • graphviz
  • six
  • sys

How to install library graphviz:

  • download the Source code for library graphviz at https://graphviz.org/download/
  • download the version based on your operating system
  • install file.exe until success

  • write in cell at your jupyter notebook:

    • import os
    • os.environ["PATH"] += os.pathsep + 'C:\Program Files (x86)\Graphviz2.38/bin/'
In [18]:
#import os
#os.environ["PATH"] += os.pathsep + 'C:\Program Files (x86)\Graphviz2.38/bin/'
In [19]:
#Install Library: pydotplus
#If you need to install, erase #

#pip install pydotplus
In [20]:
#Use Library: six and sys

import six
import sys
In [21]:
from six import StringIO
import pydotplus
import matplotlib.image as mpimg
from sklearn import tree
%matplotlib inline
In [22]:
dot_data = StringIO()
filename = "drugtree.png"
featureNames = my_data.columns[0:5]
targetNames = my_data["Drug"].unique().tolist()
out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
graph.write_png(filename)
img = mpimg.imread(filename)
plt.figure(figsize=(100, 200))
plt.imshow(img,interpolation='nearest')
Out[22]:
<matplotlib.image.AxesImage at 0x2ad6f02a7c0>